Nothing
.onLoad <- function(lib, pkg) require("methods", quietly=TRUE)
## Class SMAPObservations
setClass("SMAPObservations",
representation(## Vector of values (ratios or log2-ratios)
value="numeric",
## Vector of chromosomes
chromosome="character",
## Vector of unique chromosomes
chroms="character",
## Vector of chromosome start positions
chrom.start="numeric",
## Vector of start positions
startPosition="numeric",
## Vector of end positions
endPosition="numeric",
## Optional slots:
## Assay name
name="character",
## Vector of reporter ids
reporterId="character",
## Derived slots
## Vector of distance between clones
distance = "numeric",
## Vector overlapping spots
overlapIds="numeric",
## Vector of overlaps
overlaps="numeric",
## Vector of start indices for each clone
## in the previous two vectors
startOverlaps="numeric",
## Vector of number of overlaps for each clone
noOverlaps="numeric",
noObservations="numeric"
))
## Class SMAPHMM
setClass("SMAPHMM",
representation(## Matrix of transition probabilities
A="matrix",
## Vector of initial probabilities
Pi="numeric",
## List of dparam objects for each distribution
Phi="list",
## Length of states vector
noStates="numeric",
## Matrix of transition probabilities
Z="matrix",
## Vector of initial probabilities
Y="numeric",
eta="ANY",
grad="ANY"
))
## Class SMAPProfile
setClass("SMAPProfile",
representation(## SMAPHMM
HMM="SMAPHMM",
## SMAPObservations
observations="SMAPObservations",
## Joint posterior log probability
P="numeric",
## State sequence
Q="numeric",
## Name
name="character"
))
## Class SMAPProfiles
setClass("SMAPProfiles",
representation(name="character"),
contains="list")
## Class grad
setClass("grad",
representation(## Matrix of transition probabilities
A="matrix",
## Vector of initial probabilities
Pi="numeric",
## List of dparam objects for each distribution
Phi="list"
))
## Class eta
setClass("eta",
representation(value="numeric",
## Matrix of transition probabilities
A="matrix",
## Vector of initial probabilities
Pi="numeric",
## List of dparam objects for each distribution
Phi="list"
))
## Class gaussparam
setClass("gaussparam",
representation(mean="numeric",
sd="numeric"))
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